{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Jupyter Notebook version of Menu_stats.py\n",
"\n",
"(Version 1.0)\n",
"\n",
"![](\"pictures/TDI_logo.png\")
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### This notebook runs a A/B testing of using static menu versus dynamic menu. \n",
"\n",
"**Explain the ML project here with a detailed description...**\n",
"\n",
"> Add here later...\n",
"\n",
"\n",
"**Explain this notebook's results here...**\n",
"\n",
"> The backtesting case assumes:\n",
"\n",
"> * Two set of menus: static menu (regular), vs. dynamic menu (adjusted)\n",
"> * In both cases customers arrive at the same frequency\n",
"> * When customer waiting queue exceeds a thredshold we adjust menus.\n",
"> * Customer makes random selection on the menu, the promoted itmes will be ordered with a higher probability.\n",
"> * On adjusted menu, we promote items that takes less time to prepare.\n",
"> * On regular menu, we promote items that generates most profits.\n",
"\n",
"> We demonstrate the effect of having a dynamic menu on all of the following metrics:\n",
"\n",
"> * cumulative profits\n",
"> * cumulative waiting time\n",
"> * profit per time unit\n",
"> * next customer's waiting time\n",
"> * number of customer in queue\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"#!/usr/bin/env python3\n",
"# -*- coding: utf-8 -*-\n",
"\"\"\"\n",
"Created on Mon May 25 13:19:07 2020\n",
"@author: root\n",
"\"\"\"\n",
"# run this command to have pop up animation window in Spyder: %matplotlib qt\n",
"%matplotlib inline\n",
"\n",
"\n",
"# import libraries\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.animation as animation\n",
"\n",
"# ignore warnings\n",
"import warnings\n",
"warnings.filterwarnings(\"ignore\")\n",
"\n",
"# set the seed\n",
"np.random.seed(42)\n",
"import pandas as pd\n",
"#import os\n",
"#os.chdir('/Users/georgehan/GitHub/menu')\n",
"#os.getcwd()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Set up a simple menu for a salad restaurant:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Name Profit_Index Time_Index\n",
"0 French Sardines Salad 28 20\n",
"1 Lamb Chop Salad 27 19\n",
"2 Lobster Salad 26 15\n",
"3 Crab Meat Salad 25 15\n",
"4 Chicken salad 18 10\n",
"5 German fried potatoes Salad 17 8\n",
"6 Egg Salad 15 7\n",
"7 Organic Green Salad 14 5\n"
]
}
],
"source": [
"menu = [['French Sardines Salad', 28, 20], \n",
" ['Lamb Chop Salad', 27, 19], \n",
" ['Lobster Salad', 26, 15],\n",
" ['Crab Meat Salad', 25, 15],\n",
" ['Chicken salad', 18, 10], \n",
" ['German fried potatoes Salad', 17, 8],\n",
" ['Egg Salad', 15, 7], \n",
" ['Organic Green Salad', 14, 5]\n",
" ] \n",
"\n",
"df = pd.DataFrame(menu, columns = ['Name', 'Profit_Index', 'Time_Index']) \n",
"print(df)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Menu regular vs fast:\n",
"\n",
"Add more description here..."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"def normalize(df):\n",
" profit_scale = max(df['Profit_Index'])\n",
" time_scale = max(df['Time_Index'])\n",
" df['Profit_Index'] = df['Profit_Index'].astype(float)/profit_scale\n",
" df['Time_Index'] = df['Time_Index'].astype(float)/time_scale\n",
" return df\n",
"\n",
"def menu_regular(df):\n",
" profits = df['Profit_Index'].tolist()\n",
" profits.extend([profits[0], profits[0]])\n",
" times = df['Time_Index'].tolist()\n",
" times.extend([times[0], times[0]])\n",
" return profits, times\n",
"\n",
"def menu_fast(df):\n",
" profits = df['Profit_Index'].tolist()\n",
" profits.extend([profits[-1], profits[-1]])\n",
" times = df['Time_Index'].tolist()\n",
" times.extend([times[-1], times[-1]])\n",
" return profits, times\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"def menu_simulation(df, N, rate, l, seed=42):\n",
" \"\"\"Runs a simulated crowd of customer.\n",
" \n",
" :param int N : number of discrete steps\n",
" :param int T: number of continuous time steps\n",
" :param int rate: frequency (minutes) for customer arriving\n",
" :feed list of Profit_Indexs and Time_Indexes in df format\n",
" :param int seed: initial seed of the random generator\n",
" :returns outputs time series: \n",
" cumulative profits, cumulative waiting time, profit per time unit\n",
" next customer's waiting time, number of customer in queue\n",
" \"\"\"\n",
" # set the seed\n",
" np.random.seed(seed)\n",
" \n",
" unit_profit = []\n",
" profits_output = []\n",
" waiting_times_output = []\n",
" curr_waiting_times = []\n",
" queue_output = []\n",
" profits, times = menu_regular(df)\n",
" total_profit = 0\n",
" total_waiting_time = 0\n",
" queue = 0\n",
" \n",
" for t in range(0, N):\n",
" if t%rate == 0:\n",
" queue += 1\n",
" \n",
" if t >= total_waiting_time:\n",
" random_dish = np.random.randint(0, 10)\n",
" #print(random_dish, end = ' ')\n",
" total_profit += profits[random_dish]\n",
" total_waiting_time += times[random_dish]\n",
" queue -= 1\n",
" #print(total_waiting_time, end = ' ')\n",
" else:\n",
" pass\n",
" \n",
" profits_output.append(total_profit)\n",
" unit_profit.append(total_profit/(t+1))\n",
" waiting_times_output.append(total_waiting_time)\n",
" curr_waiting_times.append(total_waiting_time-t)\n",
" queue_output.append(queue)\n",
" \n",
" return profits_output, waiting_times_output, unit_profit, curr_waiting_times, queue_output\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Menu simulation: regular vs fast\n",
"\n",
"\n",
"Add more description here..."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"def menu_simulation_adjusted(df, N, rate, l, seed=42):\n",
" \"\"\"Runs a simulated crowd of customer.\n",
" \n",
" :param int N : number of discrete steps\n",
" :param int T: number of continuous time steps\n",
" :param int rate: frequency (minutes) for customer arriving\n",
" :feed list of Profit_Indexs and Time_Indexes in df format\n",
" :param int seed: initial seed of the random generator\n",
" :returns outputs time series: \n",
" cumulative profits, cumulative waiting time, profit per time unit\n",
" next customer's waiting time, number of customer in queue\n",
" \"\"\"\n",
" # set the seed\n",
" np.random.seed(seed)\n",
" \n",
" unit_profit = []\n",
" profits_output = []\n",
" waiting_times_output = []\n",
" curr_waiting_times = []\n",
" queue_output = []\n",
" profits, times = menu_regular(df)\n",
" total_profit = 0\n",
" total_waiting_time = 0\n",
" queue = 0\n",
" \n",
" for t in range(0, N):\n",
" if t%rate == 0:\n",
" queue += 1\n",
" \n",
" if t >= total_waiting_time:\n",
" random_dish = np.random.randint(0, 10)\n",
" #print(random_dish, end = ' ')\n",
" total_profit += profits[random_dish]\n",
" total_waiting_time += times[random_dish]\n",
" queue -= 1\n",
" #print(total_waiting_time, end = ' ')\n",
" else:\n",
" if queue > l:\n",
" profits, times = menu_fast(df)\n",
" elif queue < l:\n",
" profits, times = menu_regular(df)\n",
" pass\n",
" \n",
" profits_output.append(total_profit)\n",
" unit_profit.append(total_profit/(t+1))\n",
" waiting_times_output.append(total_waiting_time)\n",
" curr_waiting_times.append(total_waiting_time-t)\n",
" queue_output.append(queue)\n",
" \n",
" return profits_output, waiting_times_output, unit_profit, curr_waiting_times, queue_output\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Average wait time and Total profit: regular vs fast\n",
"\n",
"\n",
"Add more description here..."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Average wait time: 13.157894736842104 minutes. \n",
"Number of customer served: 76\n",
"Total profit: $1679\n"
]
}
],
"source": [
"seed = 42\n",
"N = 1000 # number of discret points\n",
"t = np.linspace(0.0, N, N)\n",
"P, T, U, C, Q = menu_simulation(df, N, 10, 1.0, seed)\n",
"\n",
"plt.figure(figsize=(14, 6))\n",
"plt.step(t, P, where='mid', lw=1, color='#0492c2')\n",
"\n",
"plt.figure(figsize=(14, 6))\n",
"plt.step(t, Q, where='mid', lw=1, color='#0492c2')\n",
"plt.show() \n",
"\n",
"\n",
"print(\"Average wait time: {} minutes. \\nNumber of customer served: {}\".format(N/len(list(set(T))), len(list(set(T)))))\n",
"print(\"Total profit: ${}\".format(P[-1]))\n"
]
},
{
"cell_type": "code",
<<<<<<< HEAD
"execution_count": 7,
=======
"execution_count": 13,
>>>>>>> 63e6a56e547f828946e94092cf76fef85d6d49d5
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Average wait time: 10.75268817204301 minutes. \n",
"Number of customer served: 93\n",
"Total profit: $1832\n"
]
}
],
"source": [
"P_adjusted, T_adjusted, U_adjusted, C_adjusted, Q_adjusted = menu_simulation_adjusted(df, N, 10, 2, seed)\n",
"\n",
"plt.figure(figsize=(14, 6))\n",
"plt.step(t, np.array(P_adjusted), where='mid', lw=1, color='#0492c2')\n",
"\n",
"plt.figure(figsize=(14, 6))\n",
"plt.step(t, Q_adjusted, where='mid', lw=1, color='#0492c2')\n",
"plt.show() \n",
"\n",
"\n",
"print(\"Average wait time: {} minutes. \\nNumber of customer served: {}\".format(N/len(list(set(T_adjusted))), len(list(set(T_adjusted)))))\n",
"print(\"Total profit: ${}\".format(P_adjusted[-1]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Static Plot: Profit per time unit comparison\n",
"\n",
"\n",
"Add more description here..."
]
},
{
"cell_type": "code",
<<<<<<< HEAD
"execution_count": 8,
=======
"execution_count": 21,
>>>>>>> 63e6a56e547f828946e94092cf76fef85d6d49d5
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"W = np.exp(np.array(P)/N) # take log scale of profits for better display of the differences\n",
"min_W = np.min(W) # min of W\n",
"X = np.exp(np.array(P_adjusted)/N)\n",
"max_X = np.max(X)\n",
"#X = drifted_brownian_motion(mu, sigma, N, T, seed) # dynamic version\n",
"#max_X = np.max(X) # max of X\n",
"\n",
"\n",
"# plot the two Time Series of profits per unit time\n",
"\n",
"# formatting options\n",
"plt.figure(figsize=(14, 6))\n",
"plt.title('Static Menu vs. Dynamic Menu', fontsize=24)\n",
"plt.xlabel('Time', fontsize=22)\n",
"plt.ylabel('Profits in $', fontsize=22)\n",
"#plt.xticks(np.linspace(0, N, 2*T + 1), fontsize=18)\n",
"plt.yticks(fontsize=18)\n",
"\n",
"plt.grid(True, which='major', linestyle='--', color='black', alpha=0.6)\n",
"plt.step(t, W, where='mid', lw=1, color='#0492c2') # Time Series with static menu\n",
"plt.step(t, X, where='mid', lw=1, color='#ff4500') # Time Series with dynamic menu\n",
"plt.plot([t[800], t[800]], [W[800], X[800]], 'ko-', lw=2) # vertical black line\n",
"plt.plot([t[800], t[800]], [W[800], X[800]], 'ko-', lw=2) # vertical black line\n",
"\n",
"plt.text(t[800]-100, (W[800] + X[800])/2-1, 'Diff. in ${:.4f}'.format(X[800]-W[800]), fontsize=18) # the text next to the line\n",
"plt.show()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Animation Plot: Profit per time unit comparison\n",
"\n",
"Can insert GIF here if Animation takes too long to generate\n",
"\n",
"Add more description here..."
]
},
{
"cell_type": "code",
<<<<<<< HEAD
"execution_count": 9,
=======
"execution_count": 23,
>>>>>>> 63e6a56e547f828946e94092cf76fef85d6d49d5
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"MovieWriter imagemagick unavailable.\n"
]
},
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
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